﻿Expertise management plays an important role in organizations. It aims to improve the performance of organizations by facilitating effective utilization of experts’ knowledge. Due to information overload problem and implicit nature of experts’ knowledge, it is difficult for users to find experts with required expertise. Expertise recommendation as a subfield of expertise management tries to alleviate the information overload problem and suggest users with experts who might satisfy their needs.
Previous research on expertise recommendation can be divided into two research streams. The first stream focuses on exploring social connections of experts, and the second one concentrates on investigating the content of the experts’ knowledge. However, these two streams of research on expertise recommendation are seldom integrated. Besides, previous research has usually used the exact matching to find experts, ignoring semantic analysis of their expertise, which is necessary for identifying similarities in terms of content.
This thesis presents a network based approach which combines social relations and semantic analysis for expertise recommendation in academic contexts. Social network analysis is used to represent communication and other relationships among researchers, and semantic analysis is used to capture the content of researchers’ expertise.
Based on the proposed network based approach, this thesis also investigates how the proposed approach can be used to recommend researchers in research communities and recommend reviewers in peer review settings through case studies.
Keywords: Network analysis, semantic analysis, expertise recommendation, reviewer recommendation, expertise management